The　current　evaluation　models　for　earth-rock　dam　compaction　quality　seldom　incorporate　parameter　uncertainty　considerations.　Additionally,　the　existing　models　frequently　demonstrate　constrained　prediction　accuracy　and　generalization　capabilities.　To　resolve　these　issues,　we　present　an　intelligent　evaluation　method　for　the　compaction　quality　of　earth-rock　dams　that　explicitly　accounts　for　parameter　uncertainty.　The　method　utilizes　a　dynamic　inertia　weight,　an　adaptive　factor,　and　a　differential　evolution　strategy　to　enhance　the　search　capability　of　the　firefly　algorithm.　Furthermore,　the　random　forest　(RF)　algorithm＇s　Ntree　and　Mtry　parameters　are　adaptively　optimized　through　the　improved　firefly　algorithm　(IFA)　to　develop　a　dam　compaction　quality　prediction　model.　This　model　aims　to　reveal　the　complex　nonlinear　mapping　relationship　between　input　influencing　factors,　such　as　compaction　parameters,　material　source　parameters,　and　meteorological　factors,　and　the　compaction　quality.　The　proposed　model　improves　the　prediction　accuracy,　generalization　ability,　and　robustness.　The　improved　firefly　optimization-based　random　forest　(IFA-RF)　is　applied　in　practical　engineering　projects,　and　the　results　validate　that　this　method　can　reliably　and　accurately　predict　the　compaction　quality　of　earth-rock　dam　construction　in　real　time　(R　=　0.90107,　MSE　=　0.0000602,　p　=　0.000)　and　thereby　guide　remedial　measures　to　ensure　engineering　safety　and　quality　compliance.